experimental design for motor imagery testing and training

Motor Imagery Using VR Digital Avatars in a Brain-Computer Interface System

Rehabilitation paradigms for patients with neurological impairments are undergoing a transformative shift with the integration of advanced technologies such as brain-computer interfaces (BCI) and immersive virtual reality (VR), offering a more nuanced and effective method for fostering recovery. Motor imagery (MI), the technique at the heart of BCI-based rehabilitation, involves the mental rehearsal of physical movements without actual motor execution1. MI exploits a neural mechanism where imagining a movement triggers a pattern of brain activity that closely mirrors that of performing the physical action itself2,3,4. Specifically, engaging in MI leads to a phenomenon known as event-related desynchronization (ERD) in the alpha (8-13 Hz) and beta (13-25 Hz) frequency bands of the brain&#39;s electrical activity5,6,7. ERD is indicative of a suppression of the baseline brain rhythms, a pattern also observed during actual movement, thereby providing a neural substrate for the use of MI within BCI-assisted rehabilitation frameworks7. Such a similarity in cortical activation between MI and physical movement suggests that MI can effectively stimulate the neural networks involved in motor control, making it a valuable tool for patients with motor deficits8. Furthermore, the practice of MI has been extended beyond mere mental rehearsal to include action observation strategies9. Observing the movement of task-related body parts or actions in others can activate the mirror neuron network (MNN), a group of neurons that respond both to action observation and execution9. Activation of the MNN through observation has been demonstrated to induce cortical plasticity, as evidenced by various neuroimaging modalities, including functional MRI10, positron emission tomography11, and transcranial magnetic stimulation12. The evidence supports the notion that MI training, enhanced by action observation, can lead to significant neural adaptation and recovery in affected individuals.
Virtual reality technology has revolutionized the realm of MI-based rehabilitation by offering an immersive environment that enhances the sense of body ownership and blurs the distinctions between the real and virtual worlds13,14,15. The immersive quality of VR makes it an effective tool for action observation and motor imagery practice, as it allows participants to perceive the virtual environment as real15. Research has shown that VR devices have a more pronounced effect on MI training compared to traditional 2D monitor displays15,16. Such findings are evidenced by enhanced neural activity, such as increased ERD amplitude ratios in the sensorimotor cortex, highlighting the benefits of higher immersion levels in stimulating brain activity during visually guided MI exercises16. The system aids in improving MI performance for tasks involving arm or limb movements by providing direct feedback, thereby enhancing the rehabilitation process16,17. The synergy between MI and VR emphasizes integrating sensory, perceptual, cognitive, and motor activities18,19. The combination has been particularly beneficial for stroke survivors20,21 and war veterans22, as studies have shown that integrating VR into MI-based rehabilitation protocols can significantly reduce rehabilitation time and improve recovery outcomes. The unique feature of VR in rehabilitation lies in its ability to create a sense of presence within a specifically designed virtual environment, enhancing the rehabilitation experience that is further augmented by the inclusion of virtual avatars representing the user&#39;s body, which has been increasingly utilized in motor rehabilitation studies23. These avatars offer a realistic three-dimensional representation of limb movements, aiding in MI and significantly impacting motor cortex activation. By allowing participants to visualize their virtual selves performing specific tasks, VR not only enriches the MI experience but also fosters a more rapid and effective neural reorganization and recovery process24. The implementation of virtual avatars and simulated environments in MI training emphasizes the natural and integrated use of virtual bodies within immersive virtual worlds.
Despite the remarkable advantages of BCI-based control of 3D avatars in MI for rehabilitation, a significant limitation remains in the predominant use of offline methodologies. Currently, most BCI applications involve capturing pre-recorded electroencephalography (EEG) data that is subsequently utilized to manipulate an avatar24,25. Even in scenarios where real-time avatar control is achieved, these avatars are often generic and do not resemble the participants they represent23. This generic approach misses a critical opportunity to deepen the immersion and sense of body ownership, which is crucial for effective rehabilitation24. The creation of a 3D avatar that mirrors the exact likeness of the subject could significantly enhance the immersive experience of the experience16. By visualizing themselves in the virtual world, participants could foster a stronger connection between their imagined and actual movements, potentially leading to more pronounced ERD patterns and, thus, more effective neural adaptation and recovery16. By advancing towards real-time control of personalized 3D avatars, the field of BCI and VR can significantly improve rehabilitation paradigms, offering a more nuanced, engaging, and efficacious method for patient recovery.
The current manuscript presents the creation, design, and technological aspects of both hardware and software of the VR-based real-time BCI control of 3D avatars, highlighting its innovative results that support its integration into motor rehabilitation settings. The proposed system will utilize electroencephalography (EEG) to capture motor imagery signals generated by the subject, which will then be used to control the movements and actions of the avatar in real time. The current approach will combine the advanced capabilities of VR technology with the precision of EEG in recognizing and interpreting brain activity related to imagined movements, aiming to create a more engaging and effective interface for users to interact with digital environments through the power of their thoughts.

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

My research in neuro rehabilitation centers around improving upper limb function for individuals with neurological injuries or disorders using a combination of EEG, motor imagery, and virtual reality technologies. The primary goal is to understand how these technologies can be integrated to enhance the effectiveness of motor skills rehabilitation. Virtual reality environments have become more sophisticated, enabling realistic simulations, tailored to individual rehabilitation needs, which help in refining motor skills in a nuanced manner.Hybrid systems that combine VR with other technologies like robotic arms and haptic feedback devices are also on the rise. One of the current experimental challenges with the use of brain computer interfaces revolves around the issue of intersubject variability and the need for extensive training. Each individual's brain signal can be vastly different, which means that BCIs often need to be extensively customized or calibrated for each user.Current treatments, often like the immersion and interactivity necessary for maximum efficacy. To fill this gap, our protocol utilizes motor imagery with an innovative twist, integrating digital twins represented by personalized 3D avatars in a virtual reality setting. This integration enhances immersion, making the rehabilitation process not just a mental exercise but also an engaging experience.Our research protocol offers significant advantages over other techniques, particularly in terms of ease of set and cost effectiveness. A standout feature is the creation and utilization of 3D avatars that closely resemble the subjects. This is achieved using simple, readily available tools and software that can generate personalized avatars from basic input data such as photographs.

Experimental Design for Motor Imagery Testing and Training

To begin, open the software tool to design and run motor imagery scenarios. Navigate to File and load the six motor imagery BCI scenarios labeled Signal verification, Acquisition, CSP training, Classifier training, Testing, and Confusion matrix. Navigate to the Signal Verification scenario and apply a Band Pass filter between 1 to 40 hertz with a filter order of 4 to the raw signals, using designer boxes.Guide the participants to undergo motor imagery tasks, imagining hand movements in response to visual cues. Open the file for Motor Imagery Training and display the prepared 3D avatar standing over a set of bongos through the VR headset. Navigate to the Acquisition Scenario and double-click the Graz Motor Imagery Stimulator to configure the box.Configure 50 trials of five second each for both left and right hand movements. Incorporate a 22nd baseline period followed by intervals of 10 seconds rest after every 10 trials to avoid mental fatigue. Configure the left and right hand trials to be randomized and add a cue before the trial indicating the hand to be imagined.Connect an OSC box with the IP address and port to transmit the cue for the hand to be imagined to the motor imagery training game engine program. Then sanitize the VR headset with wipes and place it on the participant's head to facilitate an immersive interaction while capturing EEG data. Direct the participants to imagine executing the movement of their hand along with the 3D avatar, following the same pace as the avatar when it hits the bongo with the corresponding hand with a text cue displaying which hand is to be imagined.Following the acquisition, run the CSP Training Scenario to analyze the EEG data from the acquisition stage. Create filters to distinguish between left and right hand imagery and compute CSP. After the CSP training, navigate to the Classifier Training scenario and run it to prepare the system for real-time avatar control.Then navigate to the testing scenario and allow the participants to control their 3D avatars in real time using brain computer interface technology. To interpret the imagined actions in real time load the classifiers trained during the scenario on EEG data in the appropriate boxes. Brief participants on the testing procedure emphasizing the need to clearly imagine hand movements as prompted by text cues.Conduct 20 trials for each participant divided equally between imagining movements of the left and right hand and randomized. Connect and configure an OSC box to transmit the cue information, which will be displayed as text and indicate the hand to be imaged in the game engine program. Connect to another OSC box to transmit the predicted value for the left and right hand movements for the game engine program.Run the testing scenario and the motor imagery testing game engine program. Observe that the program plays the corresponding animation based on hand movement. Five healthy adults aged 21 to 38 participated in the study under both motor imagery training and testing conditions.An average confusion matrix for all subjects was used to evaluate the classifiers accuracy in distinguishing between left and right motor imagery signals during both sessions. Topographical patterns of CSP weights from motor imagery training were visualized for both motor imagery directions. A time frequency analysis was conducted on EEG data from contralateral sensor motor areas to identify event related spectral perturbations during motor tasks.

experimental-design-for-motor-imagery-testing-and-training

Research

JoVE Journal

Bioengineering

32 visualizações. Para começar, abra a ferramenta de software para projetar e executar cenários de imagens do motor. Navegue até Arquivo e carregue os seis cenários BCI de imagens motoras rotulados Verificação de sinal, Aquisição, Treinamento CSP, Treinamento de classificador, Teste e Matriz de confusão. Navegue até o cenário de verificação de sinal e aplique um filtro de passagem de banda entre 1 e 40 hertz com uma ordem de filtro de 4 aos sinais brutos, usando c...